Creating a training tool

ABSTRACT

Techniques for creating a training technique for an individual are provided. The techniques include obtaining video of one or more events and information from a transaction log that corresponds to the one or more events, wherein the one or more events relate to one or more actions of an individual, classifying the one or more events into one or more event categories, comparing the one or more classified events with an enterprise best practices model to determine a degree of compliance, examining the one or more classified events to correct one or more misclassifications, if any, and revise the one or more event categories with the one or more corrected misclassifications, if any, and using the degree of compliance to create a training technique for the individual.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is related to U.S. patent application entitled“Generating an Alert Based on Absence of a Given Person in aTransaction,” identified by attorney docket number END920080403US1, andfiled concurrently herewith, the disclosure of which is incorporated byreference herein in its entirety.

Additionally, the present application is related to U.S. patentapplication entitled “Using Detailed Process Information at a Point ofSale,” identified by attorney docket number END920080404US1, and filedconcurrently herewith, the disclosure of which is incorporated byreference herein in its entirety.

The present application is also related to U.S. patent applicationentitled “Automatically Calibrating Regions of Interest for VideoSurveillance,” identified by attorney docket number END920080402US1, andfiled concurrently herewith, the disclosure of which is incorporated byreference herein in its entirety.

FIELD OF THE INVENTION

The present invention generally relates to information technology, and,more particularly, to retail loss prevention.

BACKGROUND OF THE INVENTION

Details of the checkout operations logs are useful for educating andmonitoring employees, shoppers and managers. Retailers need to ensurethat checkout station employees are complying with an enterprise bestpractices model. With existing approaches, however, these tools areinformal and not-scalable. Also, failure to comply with best practicescan result in lower throughput, customer dissatisfaction, damage tomerchandise, damage to property, cashier and/or customer injury, etc.

A transaction log (TLOG) can be monitored to guess or estimate a degreeof compliance (for example, one can analyze actual scans per minuteversus ideal scans per minute). However, the TLOG does not containpurely visual content (that is, any behavior that does not have acorresponding transactional entry), such as the position or orientationof people around the checkout station.

Also, if a human directly observes the cashier, the cashier's behaviormay change as the result of being observed. More problematic is the factthat a supervisor likely has other duties, has a limited ability tomaintain sustained attention and cannot observe every cashier at allwork hours. Additionally, as the number of lanes to monitor increases,examining all of these events becomes disadvantageously time-consuming.

SUMMARY OF THE INVENTION

Principles of the present invention provide techniques for creating atraining tool.

An exemplary method (which may be computer-implemented) for creating atraining technique for an individual, according to one aspect of theinvention, can include steps of obtaining video of one or more eventsand information from a transaction log that corresponds to the one ormore events, wherein the one or more events relate to one or moreactions of an individual, classifying the one or more events into one ormore event categories, comparing the one or more classified events withan enterprise best practices model to determine a degree of compliance,examining the one or more classified events to correct one or moremisclassifications, if any, and revise the one or more event categorieswith the one or more corrected misclassifications, if any, and using thedegree of compliance to create a training technique for the individual.

One or more embodiments of the invention or elements thereof can beimplemented in the form of a computer product including a computerusable medium with computer usable program code for performing themethod steps indicated. Furthermore, one or more embodiments of theinvention or elements thereof can be implemented in the form of anapparatus or system including a memory and at least one processor thatis coupled to the memory and operative to perform exemplary methodsteps.

Yet further, in another aspect, one or more embodiments of the inventionor elements thereof can be implemented in the form of means for carryingout one or more of the method steps described herein; the means caninclude hardware module(s), software module(s), or a combination ofhardware and software modules.

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating exemplary architecture, according to anembodiment of the present invention;

FIG. 2 is a diagram illustrating an exemplary retail checkoutprogression, according to an embodiment of the present invention;

FIG. 3 is a diagram illustrating an exemplary physical architectureoverview, according to an embodiment of the present invention;

FIG. 4 is a diagram illustrating a system for creating a trainingtechnique for an individual, according to an embodiment of the presentinvention;

FIG. 5 is a diagram illustrating a statistical learning technique in theinitialization phase, according to an embodiment of the presentinvention;

FIG. 6 is a flow diagram illustrating techniques for creating a trainingtechnique for an individual, according to an embodiment of the presentinvention; and

FIG. 7 is a system diagram of an exemplary computer system on which atleast one embodiment of the present invention can be implemented.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Principles of the present invention include constructing a trainingdevice. In one or more embodiments of the invention, a training deviceis constructed for a retail checkout environment. By way ofillustration, the techniques described herein can include avisually-detailed analysis of checkout events and their automaticcomparison with the enterprise or store policies that provide a richfeedback for employee, shopper and/or manager training, education,feedback and/or re-training.

One or more embodiments of the invention use a camera to obtain adetailed description and timing of checkout events, as well as use anenterprise best practices model for comparison. The video isautomatically processed, and the resulting video analysis is comparedwith the enterprise best practices model. Additionally, a human cansupervise and/or analyze the automated comparison for base-lining,training, re-training. Further, the techniques detailed herein can beiterated at user preference.

One or more embodiments of the invention can also provide scaleabletechniques for evaluating employee compliance with an enterprise bestpractices model. In contrast to disadvantageous existing approaches thatrely on estimation, the techniques described herein includes a layer ofvisual detail to data mining of a transaction log (TLOG). One or moreembodiments of the invention can also incorporate reinforcementlearning. Additionally, one or more embodiments of the invention can beimplemented specifically within the context of a checkout region in aretail environment, and therefore, for example, one can assume certaincharacteristics and activities of the scene (for example, cashier workarea, register, barcode scanner, etc.).

As described herein, one or more embodiments of the invention includemonitoring a checkout area by video camera and using a checkouttransaction log instrumented to capture a description of events.Additionally, the techniques detailed herein can include using a modelof checkout model events, an enterprise best practices model, a visualanalytic engine to analyze video of the checkout, a visual analyticengine to detect checkout events, a visual analytic engine to categorizeeach detected checkout event as one of the model events, and a visualanalytic engine to rate the categorization based on a metric.

One or more embodiments of the invention can also include relating thevisual events with the transaction log events, generating a revisedtransaction log, generating a compliance report based on the enterprisebest practices model, and generating a baseline per employee. A humanuser (for example, a supervisor) can monitor the revised transaction logevents steered by categorization metrics, as well as use the discrepancyof statistics of a specific employee to the baseline to train, re-trainand/or educate employees, shoppers, and/or managers.

FIG. 1 is a diagram illustrating exemplary architecture, according to anembodiment of the present invention. By way of illustration, FIG. 1depicts a server network and a retail network. The server networkincludes a camera 102, which feeds to visual processing in step 104,which, along with an item barcode 108, leads to a rich log 106. Also,item barcodes can be obtained from different points in the retailnetwork such as, for example, getting TLOG from a point-of-sale's(POS's) scanner port 114, intercepting and extracting TLOG from thenetwork between POS 110 (which includes a printer port 112) and POScontroller 116, and obtaining TLOG from an offline TLOG data repository118.

Within the context of an ordinary retail checkout environment, a numberof processes can occur. For example, a shopper may enter a queue, wait,empty his or her cart/basket, present any pre-transaction material (forexample, a loyalty card), scan items, pay for items and leave.Additionally, a cashier may, for example, seek or presentidentification, wait for the customer to empty his or her cart/basket,load the cash register, unload the cash register, count money, callanother cashier, indicate that a lane is active or inactive, call asupervisor, void a transaction and/or item, take payment, seek paymentand bag items for a customer. Further, a supervisor may, for example,override a situation.

FIG. 2 is a diagram illustrating an exemplary retail checkoutprogression, according to an embodiment of the present invention. By wayof illustration, FIG. 2 depicts components such as a printer 202, lights204, an age verification element 206, a hand-scan 208 and othermiscellaneous elements 244 (for example, a hard-tag remover (often inapparel stores), a demagnetizer (high-end electronics stores), aradio-frequency identification (RFID) receiver, etc.). Also, at thebeginning of the progression, a customer may unload in step 240 an item218 onto a belt 220 or counter 222 from his or her basket 224 or cart226, and a cashier or employee may pickup in step 242 the item 218 fromthe belt 220 or counter 222. The cashier or employee, at this stage, mayalso set aside an item in step 250.

Additionally, the cashier or employee, in step 246, may get a loyaltyitem 210, a coupon 214 and/or one or more types of cards 216 from thecustomer. The cashier or employee can also scan an item in step 248and/or key-in information into the register in step 252. Further, instep 254, the cashier or employee can put down an item 228 onto a belt232 or counter 234, and/or into a bag 230, a basket 236 and/or cart 238.Also, the cashier or employee can seek payment from the customer in step256.

FIG. 3 is a diagram illustrating an exemplary physical architectureoverview, according to an embodiment of the present invention. By way ofillustration, FIG. 3 depicts steps that can take place in a genericsetting and steps that can occur in a retail specific setting. Asillustrated in FIG. 3, a generic setting can include obtaining a processrequest in step 302, obtaining a process definition in step 306 andperforming a process execution in step 304, which can includeidentifying events (for example, indicators or co-indicators ofbehaviors) in step 308. A generic setting can also include a camera 310.

Additionally, a generic setting can include creating a process log instep 312, analyzing the process in step 314 and creating a smart log instep 316, wherein the smart log can have capabilities such as, forexample, browsing, providing feedback and mining.

A retail specific setting can include a point-of-sale station 318, whichcan produce events in step 324 such as, for example, override, void,change given, price check and coupon. A retail specific setting can alsoinclude a camera 322. Further, one can create a transaction log (TLOG)in step 320, analyze the transaction in step 326 and created a smart login step 328, wherein the smart log can have capabilities such as, forexample, browsing, reconciling data and mining.

FIG. 4 is a diagram illustrating a system for creating a trainingtechnique for an individual, according to an embodiment of the presentinvention. By way of illustration, FIG. 4 depicts a best practices model402, a compliance engine 404, a compliance report 406, a humansupervisor 408 and cashiers 410. FIG. 4 also depicts event models 412,an event classifier 414 and classified checkout events 416.Additionally, FIG. 4 depicts a video analytics engine 418, unclassifiedcheckout events 420, video 422 and a TLOG 424.

Based on video 422 and TLOG 424 input, the video analytics engine 418outputs a set of unclassified events 420. Each event is a collection oflow-level features such as, for example, shape, color, texture,location, orientation, area, motion characteristics, edges, etc. Theevent classifier 414 classifies the events based on its current set ofevent models 412 and outputs a set of classified checkout events 416(for example, a person present in cashier area, a barcode scanned,multiple people present in customer area, transaction voided, etc.).

The compliance engine 404 analyzes the classified events 416 anddetermines their degree of compliance based on a best practices model402. A compliance report 406 is generated that indicates eachindividual's degree of compliance. A human supervisor 408 can examinethe report and decides on re-training techniques for selectedindividuals (such as, for example, cashiers 410). The supervisor 408also has the ability to correct misclassifications and update the eventmodels 412 and video analytics engine 418 according to the corrections.

FIG. 5 is a diagram illustrating a statistical learning method in theinitialization phase, according to an embodiment of the presentinvention. By way of illustration, FIG. 5 depicts starting in step 502,grabbing a video frame in step 504, importing a transaction event instep 506. Also, FIG. 4 depicts a learning engine 508 as well as eventmodels 510. The learning engine 508 iteratively grabs video frames froma video source and receives transaction events as they are produced andupdates the statistical event models 510. This process can proceed untilsuch a time that the event models 510 are considered stable enough foruse in the overall system. Note, also, that the learning phase cancontinue, by way of example, in conjunction with reinforcement learningwith a human monitor involved.

FIG. 6 is a flow diagram illustrating techniques for creating a trainingtechnique for an individual (for example, an employee), according to anembodiment of the present invention. Step 602 includes obtaining videoof one or more events and information from a transaction log thatcorresponds to the one or more events, wherein the one or more eventsrelate to one or more actions of an individual. Obtaining video ofevents and information from a transaction log that corresponds to theevents can include inputting the video and transaction log informationinto a video analytics engine, wherein the video analytics engineoutputs a set of unclassified events. Each of the events includes acollection of one or more features such as, for example, shape, color,texture, location, orientation, area, one or more motioncharacteristics, edges, optical flow, color statistics, spatialgradient, temporal gradient, temporal texture, object locations, objecttrajectories, etc.

Step 604 includes classifying the one or more events into one or moreevent categories. The event categories can include, by way of example, aperson present in cashier area, a barcode scanned, multiple peoplepresent in customer area, transaction voided, a person present incustomer area, multiple people present in cashier area, a keyboardinteraction, one or more items bagged, a pick-up motion, a scan motionand a drop motion, etc. Step 606 includes comparing the one or moreclassified events with an enterprise best practices model to determine adegree of compliance. Step 608 includes examining the one or moreclassified events to correct one or more misclassifications, if any, andrevise the one or more event categories with the one or more correctedmisclassifications, if any.

Step 610 includes using the degree of compliance to create a trainingtechnique for the individual. Using the degree of compliance to create atraining technique for the individual can include a human supervisorexamining the degree of compliance to create a training technique forthe individual. Further, one or more embodiments of the inventioninclude automatically learning one or more statistical models of one ormore model events (for example, during a user-determined time periodfollowing system initialization and subsequently adjusted by a humanmonitor).

By way of example only, one or more embodiments of the invention caninclude the following scenarios. The system described herein can detectthat an employee is not using a chair at his or her workstation,resulting in the employee remaining standing for a long period of time,which could possibly result in injury. As a result, the employee isinformed about the availability of seating, the types of injuries thatcan result, and is informed to use seating. Also, the system describedherein can detect that an employee has slower than normal throughput,resulting in non-optimal customer wait times. An investigation revealsthat the employee is not using the standard two-handed scanningtechnique. As a result, the technique is taught to the employee.Further, the system described herein can detect that a cash drawer isoften open when an employee is not present at the register. As a result,the employee is informed that the cash drawer should never be leftunattended.

The techniques depicted in FIG. 6 can also include generating acompliance report that indicates the degree of compliance for eachindividual. Additionally, one or more embodiments of the invention caninclude rating the classification of the events based on a metric,correcting a misclassification of an event, using the correction toupdate the generating a revised transaction log. By way of example, oneor more embodiments of the invention can include using a metric thatmeasures the degree of similarity or dissimilarity of an unclassifiedevent to event models to classify the event as one of the model eventsor optionally placing the event into a reject category (that is, it isnot similar enough to any event models). Also, one can use a metric toclassify an event within class ranking of the event according to howwell the event fits the model. Classification techniques to determinethese similarities or dissimilarities can include, by way of example andnot limitation, nearest class mean, nearest neighbors, artificial neuralnets, support vector machine, Bayesian classification, etc.

Additionally, a revised TLOG can include transactional (for example,barcode scanned, item voided, manager override, lane opened, etc.) andvisual events (for example, scan motion, customer present, cashierpresent, multiple people present in cashier area, etc.). The revisedTLOG can be input for a higher level process, such as a data miningengine, that analyzes the log based on additional input (for example, anenterprise best practices model). By way of example, in FIG. 4, anexemplary data mining engine is included in the form of a complianceengine 404.

A variety of techniques, utilizing dedicated hardware, general purposeprocessors, software, or a combination of the foregoing may be employedto implement the present invention. At least one embodiment of theinvention can be implemented in the form of a computer product includinga computer usable medium with computer usable program code forperforming the method steps indicated. Furthermore, at least oneembodiment of the invention can be implemented in the form of anapparatus including a memory and at least one processor that is coupledto the memory and operative to perform exemplary method steps.

At present, it is believed that the preferred implementation will makesubstantial use of software running on a general-purpose computer orworkstation. With reference to FIG. 7, such an implementation mightemploy, for example, a processor 702, a memory 704, and an input and/oroutput interface formed, for example, by a display 706 and a keyboard708. The term “processor” as used herein is intended to include anyprocessing device, such as, for example, one that includes a CPU(central processing unit) and/or other forms of processing circuitry.Further, the term “processor” may refer to more than one individualprocessor. The term “memory” is intended to include memory associatedwith a processor or CPU, such as, for example, RAM (random accessmemory), ROM (read only memory), a fixed memory device (for example,hard drive), a removable memory device (for example, diskette), a flashmemory and the like. In addition, the phrase “input and/or outputinterface” as used herein, is intended to include, for example, one ormore mechanisms for inputting data to the processing unit (for example,mouse), and one or more mechanisms for providing results associated withthe processing unit (for example, printer). The processor 702, memory704, and input and/or output interface such as display 706 and keyboard708 can be interconnected, for example, via bus 710 as part of a dataprocessing unit 712. Suitable interconnections, for example via bus 710,can also be provided to a network interface 714, such as a network card,which can be provided to interface with a computer network, and to amedia interface 716, such as a diskette or CD-ROM drive, which can beprovided to interface with media 718.

Accordingly, computer software including instructions or code forperforming the methodologies of the invention, as described herein, maybe stored in one or more of the associated memory devices (for example,ROM, fixed or removable memory) and, when ready to be utilized, loadedin part or in whole (for example, into RAM) and executed by a CPU. Suchsoftware could include, but is not limited to, firmware, residentsoftware, microcode, and the like.

Furthermore, the invention can take the form of a computer programproduct accessible from a computer-usable or computer-readable medium(for example, media 718) providing program code for use by or inconnection with a computer or any instruction execution system. For thepurposes of this description, a computer usable or computer readablemedium can be any apparatus for use by or in connection with theinstruction execution system, apparatus, or device.

The medium can be an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device) or apropagation medium. Examples of a computer-readable medium include asemiconductor or solid-state memory (for example, memory 704), magnetictape, a removable computer diskette (for example, media 718), a randomaccess memory (RAM), a read-only memory (ROM), a rigid magnetic disk andan optical disk. Current examples of optical disks include compactdisk-read only memory (CD-ROM), compact disk-read and/or write (CD-R/W)and DVD.

A data processing system suitable for storing and/or executing programcode will include at least one processor 702 coupled directly orindirectly to memory elements 704 through a system bus 710. The memoryelements can include local memory employed during actual execution ofthe program code, bulk storage, and cache memories which providetemporary storage of at least some program code in order to reduce thenumber of times code must be retrieved from bulk storage duringexecution.

Input and/or output or I/O devices (including but not limited tokeyboards 708, displays 706, pointing devices, and the like) can becoupled to the system either directly (such as via bus 710) or throughintervening I/O controllers (omitted for clarity).

Network adapters such as network interface 714 may also be coupled tothe system to enable the data processing system to become coupled toother data processing systems or remote printers or storage devicesthrough intervening private or public networks. Modems, cable modem andEthernet cards are just a few of the currently available types ofnetwork adapters.

In any case, it should be understood that the components illustratedherein may be implemented in various forms of hardware, software, orcombinations thereof, for example, application specific integratedcircuit(s) (ASICS), functional circuitry, one or more appropriatelyprogrammed general purpose digital computers with associated memory, andthe like. Given the teachings of the invention provided herein, one ofordinary skill in the related art will be able to contemplate otherimplementations of the components of the invention.

At least one embodiment of the invention may provide one or morebeneficial effects, such as, for example, creating a visually-detailedanalysis of checkout events and automatically comparing that analysiswith enterprise or store policies.

Although illustrative embodiments of the present invention have beendescribed herein with reference to the accompanying drawings, it is tobe understood that the invention is not limited to those preciseembodiments, and that various other changes and modifications may bemade by one skilled in the art without departing from the scope orspirit of the invention.

1. A method for creating a training technique for an individual,comprising the steps of: obtaining video of one or more events andinformation from a transaction log that corresponds to the one or moreevents, wherein the one or more events relate to one or more actions ofan individual; classifying the one or more events into one or more eventcategories; comparing the one or more classified events with anenterprise best practices model to determine a degree of compliance;examining the one or more classified events to correct one or moremisclassifications, if any, and revise the one or more event categorieswith the one or more corrected misclassifications, if any; and using thedegree of compliance to create a training technique for the individual.2. The method of claim 1, wherein obtaining video of one or more eventsand information from a transaction log that corresponds to the one ormore events comprises inputting the video and transaction loginformation into a video analytics engine, wherein the video analyticsengine outputs a set of one or more unclassified events.
 3. The methodof claim 1, wherein each of the one or more events comprises acollection of one or more features.
 4. The method of claim 3, whereinthe one or more features comprise at least one of shape, color, texture,location, orientation, area, one or more motion characteristics, one ormore edges, optical flow, one or more color statistics, spatialgradient, temporal gradient, temporal texture, one or more objectlocations and one or more object trajectories.
 5. The method of claim 1,wherein the one or more event categories comprise at least one of aperson present in cashier area, a barcode scanned, multiple peoplepresent in customer area, transaction voided, a person present incustomer area, multiple people present in cashier area, a keyboardinteraction, one or more items bagged, a pick-up motion, a scan motionand a drop motion.
 6. The method of claim 1, further comprisinggenerating a compliance report that indicates the degree of compliancefor each individual.
 7. The method of claim 1, wherein using the degreeof compliance to create a training technique for the individualcomprises a human supervisor examining the degree of compliance tocreate a training technique for the individual.
 8. The method of claim1, further comprising rating the classification of the one or moreevents based on a metric.
 9. The method of claim 1, further comprisingautomatically learning one or more statistical models of one or moremodel events.
 10. The method of claim 1, further comprising generating arevised transaction log.
 11. A computer program product comprising acomputer readable medium having computer readable program code forcreating a training technique for an individual, said computer programproduct including: computer readable program code for obtaining video ofone or more events and information from a transaction log thatcorresponds to the one or more events, wherein the one or more eventsrelate to one or more actions of an individual; computer readableprogram code for classifying the one or more events into one or moreevent categories; computer readable program code for comparing the oneor more classified events with an enterprise best practices model todetermine a degree of compliance; computer readable program forexamining the one or more classified events to correct one or moremisclassifications, if any, and revise the one or more event categorieswith the one or more corrected misclassifications, if any; and computerreadable program code for using the degree of compliance to create atraining technique for the individual.
 12. The computer program productof claim 11, wherein the computer readable program code for obtainingvideo of one or more events and information from a transaction log thatcorresponds to the one or more events comprises computer readableprogram code for inputting the video and transaction log informationinto a video analytics engine, wherein the video analytics engineoutputs a set of one or more unclassified events.
 13. The computerprogram product of claim 11, further comprising computer readableprogram code for generating a compliance report that indicates thedegree of compliance for each individual.
 14. The computer programproduct of claim 11, wherein the computer readable program code forusing the degree of compliance to create a training technique for theindividual comprises computer readable program code for a humansupervisor examining the degree of compliance to create a trainingtechnique for the individual.
 15. The computer program product of claim11, further comprising computer readable program code for rating theclassification of the one or more events based on a metric.
 16. Thecomputer program product of claim 11, further comprising computerreadable program code for generating a revised transaction log.
 17. Thecomputer program product of claim 11, further comprising computerreadable program code for automatically learning one or more statisticalmodels of one or more model events.
 18. A system for creating a trainingtechnique for an individual, comprising: a memory; and at least oneprocessor coupled to said memory and operative to: obtain video of oneor more events and information from a transaction log that correspondsto the one or more events, wherein the one or more events relate to oneor more actions of an individual; classify the one or more events intoone or more event categories; compare the one or more classified eventswith an enterprise best practices model to determine a degree ofcompliance; examine the one or more classified events to correct one ormore misclassifications, if any, and revise the one or more eventcategories with the one or more corrected misclassifications, if any;and use the degree of compliance to create a training technique for theindividual.
 19. The system of claim 18, wherein in obtaining video ofone or more events and information from a transaction log thatcorresponds to the one or more events, the at least one processorcoupled to said memory is further operative to input the video andtransaction log information into a video analytics engine, wherein thevideo analytics engine outputs a set of one or more unclassified events.20. The system of claim 18, wherein the at least one processor coupledto said memory is further operative to generate a compliance report thatindicates the degree of compliance for each individual.
 21. The systemof claim 18, wherein in using the degree of compliance to create atraining technique for the individual, the at least one processorcoupled to said memory is further operative to enable a human supervisorexamining the degree of compliance to create a training technique forthe individual.
 22. The system of claim 18, wherein the at least oneprocessor coupled to said memory is further operative to rate theclassification of the one or more events based on a metric.
 23. Thesystem of claim 18, wherein the at least one processor coupled to saidmemory is further operative to generate a revised transaction log. 24.The system of claim 18, wherein the at least one processor coupled tosaid memory is further operative to automatically learn one or morestatistical models of one or more model events.
 25. An apparatus forcreating a training technique for an individual, said apparatuscomprising: means for obtaining video of one or more events andinformation from a transaction log that corresponds to the one or moreevents, wherein the one or more events relate to one or more actions ofan individual; means for classifying the one or more events into one ormore event categories; means for comparing the one or more classifiedevents with an enterprise best practices model to determine a degree ofcompliance; means for examining the one or more classified events tocorrect one or more misclassifications, if any, and revise the one ormore event categories with the one or more corrected misclassifications,if any; and means for using the degree of compliance to create atraining technique for the individual.